Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy

Alsattar, Hassan A.; Qahtan, Sarah; Zaidan, Aws Alaa; Deveci, Muhammet; Martinez, Luis; Pamucar, Dragan; Pedrycz, Witold

doi:10.1016/j.eswa.2023.121300

Cited by 12 publications

(2 citation statements)

References 61 publications

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“…These works examined the practical aspects of implementing AI tools in healthcare settings, emphasizing the need for user-friendly, practical models for medical professionals. Specific AI architectures were the focus of research [33] and [34], which delved into optimizing layer structures in CNNs for better feature extraction from medical images. These findings have informed the development of sophisticated AI models capable of detecting subtle anomalies in X-ray images.…”

Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

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In the wake of the COVID-19 pandemic, the use of medical imaging, particularly X-ray radiography, has become integral to the rapid and accurate diagnosis of pneumonia induced by the virus. This research paper introduces a novel twodimensional Convolutional Neural Network (2D-CNN) architecture specifically tailored for the classification of COVID-19 related pneumonia in X-ray images. Leveraging the advancements in deep learning, our model is designed to distinguish between viral pneumonia, typical of COVID-19, and other types of pneumonia, as well as healthy lung imagery. The architecture of the proposed 2D-CNN is characterized by its depth and a unique layer arrangement, which optimizes feature extraction from X-ray images, thus enhancing the model's diagnostic precision. We trained our model using a substantial dataset comprising thousands of annotated X-ray images, including those of patients diagnosed with COVID-19, patients with other pneumonia types, and individuals with no lung infection. This dataset enabled the model to learn a wide range of radiographic features associated with different lung conditions. Our model demonstrated exceptional performance, achieving high accuracy, sensitivity, and specificity in preliminary tests.The results indicate that our 2D-CNN model not only outperforms existing pneumonia classification models but also provides a valuable tool for healthcare professionals in the early detection and differentiation of COVID-19 related pneumonia. This capability is crucial for prompt and appropriate treatment, potentially reducing the pandemic's burden on healthcare systems. Furthermore, the model's design allows for easy integration into existing medical imaging workflows, offering a practical and efficient solution for frontline medical facilities. Our research contributes to the ongoing efforts to combat COVID-19 by enhancing diagnostic procedures through the application of artificial intelligence in medical imaging.

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Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…The suspected case undergoes an X-Ray session and if more details are required, a computed tomography scan (CT-scan) session is taken. Therefore, X-ray 7 and CT scan images 8 are being used as diagnostic methods for COVID-19 and to detect the effects 9 of the virus 6 , 10 . The availability and accessibility of X-ray imaging in many imaging centers and clinics is more present even in rural regions as it is standard equipment in healthcare systems.…”

Section: Introductionmentioning

confidence: 99%

COVID‑19 detection from chest X-ray images using transfer learning

El Houby

2024

Sci Rep

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COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

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Double Uncertainty Driving and Integrated Decision-Making Under the Mixed Probabilistic Hesitant Environment

Zhou,

Luo,

2024

Int. J. Fuzzy Syst.

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Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy

Cited by 12 publications

References 61 publications

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

COVID‑19 detection from chest X-ray images using transfer learning

Double Uncertainty Driving and Integrated Decision-Making Under the Mixed Probabilistic Hesitant Environment

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